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Learning AI
·
Module 1
Foundations of Machine Learning
1
Types of Machine Learning: A Comparative Analysis
Define and distinguish between supervised, unsupervised, and reinforcement learning with examples.
2
Understanding Data Splits: Training, Validation, and Test Sets
Explain the concepts of training, validation, and test sets and their roles in model development.
3
Bias-Variance Tradeoff: Impact on Model Generalization
Describe the bias-variance tradeoff and its implications for model generalization.
4
Understanding Overfitting and Underfitting with Learning Curves
Explain overfitting and underfitting and identify them from learning curves.
5
Setting Up Your ML Project Environment
Set up a standard ML project environment with NumPy, pandas, and scikit-learn.
6
Building a KNN Baseline with Scikit-learn
Implement a k-nearest neighbors classifier using scikit-learn to establish a baseline.
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